Browsing by Author "Vathsala, H."

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    Closed Item-Set Mining for Prediction of Indian Summer Monsoon Rainfall A Data Mining Model with Land and Ocean Variables as Predictors
    (2015) Vathsala, H.; Koolagudi, S.G.
    Practical application of data mining in scientific and engineering domains, when explored, pose many problems and provide interesting results. In this paper, we attempt to mine out association rules from 37 (1969-2005) years of Indian summer monsoon rainfall data and try its applicability in helping better prediction of Indian summer monsoon rainfall. We shortlist 36 variables as possible predictors of Indian summer monsoon rainfall based on previous literature and compare prediction using all 36 variables and prediction by selected attributes from derived association rules. Results show better performance in prediction of All India region, West central region and Peninsular region rainfall when attributes selection is employed as compared to all 36 variables used for prediction. � 2015 The Authors.
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    Closed Item-Set Mining for Prediction of Indian Summer Monsoon Rainfall A Data Mining Model with Land and Ocean Variables as Predictors
    (Elsevier, 2015) Vathsala, H.; Koolagudi, S.G.
    Practical application of data mining in scientific and engineering domains, when explored, pose many problems and provide interesting results. In this paper, we attempt to mine out association rules from 37 (1969-2005) years of Indian summer monsoon rainfall data and try its applicability in helping better prediction of Indian summer monsoon rainfall. We shortlist 36 variables as possible predictors of Indian summer monsoon rainfall based on previous literature and compare prediction using all 36 variables and prediction by selected attributes from derived association rules. Results show better performance in prediction of All India region, West central region and Peninsular region rainfall when attributes selection is employed as compared to all 36 variables used for prediction. © 2015 The Authors.
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    Impact of COVID-19 on the Sectors of the Indian Economy and the World
    (Springer Science and Business Media Deutschland GmbH, 2023) Gite, R.; Vathsala, H.; Koolagudi, S.G.
    It is known that the SARS-CoV2 (More popularly known as Corona Virus) has affected the way countries function. It has influenced the general health and economy of various countries. Earlier studies have discussed the economic repercussions of various epidemics qualitatively. This paper discusses employing correlation analysis in combination with machine learning techniques to determine the impact of the virus on country’s economic health. The results are justified by the trends seen in pre-COVID, COVID and post-COVID phases there by providing a base for predicting economic conditions of the world in case of any such pandemics in the future. The study includes country-wise analysis for which fifteen country’s economic data is analyzed and sector-wise impact analysis with a specific case of India has been attempted. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches
    (Springer-Verlag Wien michaela.bolli@springer.at, 2017) Vathsala, H.; Koolagudi, S.G.
    This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969–2005). © 2016, Springer-Verlag Wien.
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    Neuro-Fuzzy Model for Quantified Rainfall Prediction Using Data Mining and Soft Computing Approaches
    (Taylor and Francis Ltd., 2023) Vathsala, H.; Koolagudi, S.G.
    In this paper, we discuss an approach that predicts the quantitative value of rainfall. The proposed algorithm uses a combination of data mining and neuro-fuzzy inference system for prediction. The model is demonstrated on north interior Karnataka (a state in India) rainfall data as a case study. This model is applicable to any geographical area provided apt predictors are included. For north interior Karnataka rainfall prediction predictors are derived from local and global climate conditions. The local condition variables are derived from the mean sea level pressure, temperature, and wind speed in south India. The global variables affecting the north interior Karnataka rainfall include, Darwin sea level pressure, the ENSO indices and southern oscillation. The data mining technique, association rule mining, is used to study the correlation among the predictors; clustering is used for predictor selection as well as membership function creation for fuzzyfication. Neuro-fuzzy inference system is further used for fine tuning the “If-then” rules and crisp value prediction of the rainfall. The prediction accuracy is observed to be good considering Tropical Meteorological Department data. © 2023 IETE.
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    NLP2SQL Using Semi-supervised Learning
    (Springer Science and Business Media Deutschland GmbH, 2021) Vathsala, H.; Koolagudi, S.G.
    Human Computer interaction has been moving towards Natural language in the modern age. SQL (Structured Query Language) is the chief database query language used today. There are many flavors of SQL but all of them have the same basic underlying structure. This paper attempts to use the Natural Language inputs to query the databases, which is achieved by translating the natural language (which in our case is English) input into the SQL (specific to MySQL database) query language. Here we use a semi-supervised learning with Memory augmented policy optimization approach to solve this problem. This method uses the context of the natural language questions through database schema, and hence its not just generation of SQL code. We have used the WikiSQL dataset for all our experiments. The proposed method gives a 2.3% higher accuracy than the state of the art semi-supervised method on an average. © 2021, Springer Nature Singapore Pte Ltd.
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    Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches
    (Elsevier Ltd, 2017) Vathsala, H.; Koolagudi, S.G.
    In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good. © 2016 Elsevier Ltd
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    Profile generation from web sources: an information extraction system
    (Springer, 2022) Ranjan, R.; Vathsala, H.; Koolagudi, S.G.
    The Internet space has a vast collection of information which is not always structured. These sources of information such as social media, news articles, blogs, speeches and videos often contain information that could be utilized to generate decision making tools such as reports about events and individuals. Using this information is a long and tedious process if done manually. Over the years, a lot of research has been done in data mining and natural language processing techniques to facilitate the consumption of this vast amount of data. The current work describes ProfileGen, an information extraction system that uses a variety of these data sources to form a profile of a given person. There are two parts to this application: The first part uses information publicly available on social media sites, news articles on news websites and blogs and compiles this information to form a corpus about the given person, and in the second part, the information is ranked using machine learning techniques, so as to provide information in the order of importance. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.